Overview

Dataset statistics

Number of variables20
Number of observations21608
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory168.0 B

Variable types

Numeric17
Categorical3

Alerts

age is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
bathrooms is highly overall correlated with age and 7 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
floors is highly overall correlated with age and 4 other fieldsHigh correlation
grade is highly overall correlated with age and 7 other fieldsHigh correlation
price is highly overall correlated with grade and 3 other fieldsHigh correlation
renovation_age is highly overall correlated with yr_renovatedHigh correlation
sqft_above is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
yr_built is highly overall correlated with age and 3 other fieldsHigh correlation
yr_renovated is highly overall correlated with renovation_ageHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.3%)Imbalance
sqft_basement has 13123 (60.7%) zerosZeros
yr_renovated has 20695 (95.8%) zerosZeros

Reproduction

Analysis started2023-11-23 07:58:45.790654
Analysis finished2023-11-23 08:00:36.613368
Duration1 minute and 50.82 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

price
Real number (ℝ)

HIGH CORRELATION 

Distinct4028
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540098.37
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:36.982532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1321837.5
median450000
Q3645000
95-th percentile1156780
Maximum7700000
Range7625000
Interquartile range (IQR)323162.5

Descriptive statistics

Standard deviation367164.57
Coefficient of variation (CV)0.67981055
Kurtosis34.578471
Mean540098.37
Median Absolute Deviation (MAD)150000
Skewness4.0237083
Sum1.1670446 × 1010
Variance1.3480982 × 1011
MonotonicityNot monotonic
2023-11-23T10:00:37.608630image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000 172
 
0.8%
450000 172
 
0.8%
550000 158
 
0.7%
500000 152
 
0.7%
425000 150
 
0.7%
325000 148
 
0.7%
400000 145
 
0.7%
375000 138
 
0.6%
300000 133
 
0.6%
525000 131
 
0.6%
Other values (4018) 20109
93.1%
ValueCountFrequency (%)
75000 1
< 0.1%
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 2
< 0.1%
86500 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7062500 1
< 0.1%
6885000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110800 1
< 0.1%
4668000 1
< 0.1%
4500000 1
< 0.1%
4489000 1
< 0.1%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3709274
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:38.075609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93010257
Coefficient of variation (CV)0.27591889
Kurtosis49.065621
Mean3.3709274
Median Absolute Deviation (MAD)1
Skewness1.9743628
Sum72839
Variance0.86509079
MonotonicityNot monotonic
2023-11-23T10:00:38.514945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 9821
45.5%
4 6881
31.8%
2 2759
 
12.8%
5 1601
 
7.4%
6 272
 
1.3%
1 199
 
0.9%
7 38
 
0.2%
0 13
 
0.1%
8 13
 
0.1%
9 6
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
0 13
 
0.1%
1 199
 
0.9%
2 2759
 
12.8%
3 9821
45.5%
4 6881
31.8%
5 1601
 
7.4%
6 272
 
1.3%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 272
 
1.3%
5 1601
 
7.4%
4 6881
31.8%
3 9821
45.5%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1147376
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:38.961625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7702384
Coefficient of variation (CV)0.3642241
Kurtosis1.2792646
Mean2.1147376
Median Absolute Deviation (MAD)0.5
Skewness0.51114612
Sum45695.25
Variance0.5932672
MonotonicityNot monotonic
2023-11-23T10:00:39.487570image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.5 5378
24.9%
1 3852
17.8%
1.75 3047
14.1%
2.25 2046
 
9.5%
2 1929
 
8.9%
1.5 1446
 
6.7%
2.75 1185
 
5.5%
3 753
 
3.5%
3.5 731
 
3.4%
3.25 589
 
2.7%
Other values (20) 652
 
3.0%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.5 4
 
< 0.1%
0.75 72
 
0.3%
1 3852
17.8%
1.25 9
 
< 0.1%
1.5 1446
 
6.7%
1.75 3047
14.1%
2 1929
 
8.9%
2.25 2046
 
9.5%
2.5 5378
24.9%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

HIGH CORRELATION 

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.959
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:40.006471image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11429.25
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1120.75

Descriptive statistics

Standard deviation918.5058
Coefficient of variation (CV)0.44159804
Kurtosis5.2422093
Mean2079.959
Median Absolute Deviation (MAD)541
Skewness1.4714675
Sum44943753
Variance843652.91
MonotonicityNot monotonic
2023-11-23T10:00:40.619334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 138
 
0.6%
1400 135
 
0.6%
1440 133
 
0.6%
1800 129
 
0.6%
1660 129
 
0.6%
1010 129
 
0.6%
1820 128
 
0.6%
1480 125
 
0.6%
1720 125
 
0.6%
1540 124
 
0.6%
Other values (1028) 20313
94.0%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

HIGH CORRELATION 

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15109.616
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:41.190510image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1801.7
Q15040
median7620
Q310690.5
95-th percentile43343.7
Maximum1651359
Range1650839
Interquartile range (IQR)5650.5

Descriptive statistics

Standard deviation41424.914
Coefficient of variation (CV)2.7416258
Kurtosis285.01868
Mean15109.616
Median Absolute Deviation (MAD)2620
Skewness13.058691
Sum3.2648859 × 108
Variance1.7160235 × 109
MonotonicityNot monotonic
2023-11-23T10:00:41.782253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 358
 
1.7%
6000 290
 
1.3%
4000 251
 
1.2%
7200 220
 
1.0%
4800 120
 
0.6%
7500 119
 
0.6%
4500 114
 
0.5%
8400 111
 
0.5%
9600 109
 
0.5%
3600 103
 
0.5%
Other values (9772) 19813
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 1
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4941457
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:42.104667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53991035
Coefficient of variation (CV)0.36135054
Kurtosis-0.48487366
Mean1.4941457
Median Absolute Deviation (MAD)0.5
Skewness0.61633974
Sum32285.5
Variance0.29150319
MonotonicityNot monotonic
2023-11-23T10:00:42.307537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10680
49.4%
2 8237
38.1%
1.5 1910
 
8.8%
3 612
 
2.8%
2.5 161
 
0.7%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
1 10680
49.4%
1.5 1910
 
8.8%
2 8237
38.1%
2.5 161
 
0.7%
3 612
 
2.8%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
3.5 8
 
< 0.1%
3 612
 
2.8%
2.5 161
 
0.7%
2 8237
38.1%
1.5 1910
 
8.8%
1 10680
49.4%

waterfront
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.6 KiB
0
21445 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21608
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21445
99.2%
1 163
 
0.8%

Length

2023-11-23T10:00:42.744809image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T10:00:43.202992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 21445
99.2%
1 163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21445
99.2%
1 163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21608
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21445
99.2%
1 163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21445
99.2%
1 163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21445
99.2%
1 163
 
0.8%

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.6 KiB
0
19485 
2
 
963
3
 
509
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21608
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19485
90.2%
2 963
 
4.5%
3 509
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Length

2023-11-23T10:00:43.631521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T10:00:44.087337image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19485
90.2%
2 963
 
4.5%
3 509
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 19485
90.2%
2 963
 
4.5%
3 509
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21608
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19485
90.2%
2 963
 
4.5%
3 509
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19485
90.2%
2 963
 
4.5%
3 509
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19485
90.2%
2 963
 
4.5%
3 509
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.6 KiB
3
14027 
4
5678 
5
1701 
2
 
172
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21608
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 14027
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Length

2023-11-23T10:00:44.550396image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T10:00:45.022180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 14027
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 14027
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21608
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14027
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 14027
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 14027
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

grade
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6566549
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:45.452883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1754836
Coefficient of variation (CV)0.15352443
Kurtosis1.1918838
Mean7.6566549
Median Absolute Deviation (MAD)1
Skewness0.7715804
Sum165445
Variance1.3817616
MonotonicityNot monotonic
2023-11-23T10:00:46.570754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 8981
41.6%
8 6066
28.1%
9 2612
 
12.1%
6 2038
 
9.4%
10 1134
 
5.2%
11 399
 
1.8%
5 242
 
1.1%
12 90
 
0.4%
4 29
 
0.1%
13 13
 
0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
4 29
 
0.1%
5 242
 
1.1%
6 2038
 
9.4%
7 8981
41.6%
8 6066
28.1%
9 2612
 
12.1%
10 1134
 
5.2%
11 399
 
1.8%
ValueCountFrequency (%)
13 13
 
0.1%
12 90
 
0.4%
11 399
 
1.8%
10 1134
 
5.2%
9 2612
 
12.1%
8 6066
28.1%
7 8981
41.6%
6 2038
 
9.4%
5 242
 
1.1%
4 29
 
0.1%

sqft_above
Real number (ℝ)

HIGH CORRELATION 

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.4148
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:47.054528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation828.14115
Coefficient of variation (CV)0.46305876
Kurtosis3.4019277
Mean1788.4148
Median Absolute Deviation (MAD)450
Skewness1.4467404
Sum38644068
Variance685817.77
MonotonicityNot monotonic
2023-11-23T10:00:47.628422image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 212
 
1.0%
1010 210
 
1.0%
1200 206
 
1.0%
1220 192
 
0.9%
1140 184
 
0.9%
1400 180
 
0.8%
1060 178
 
0.8%
1180 177
 
0.8%
1340 176
 
0.8%
1250 174
 
0.8%
Other values (936) 19719
91.3%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7880 1
< 0.1%
7850 1
< 0.1%
7680 1
< 0.1%
7420 1
< 0.1%
7320 1
< 0.1%
6720 1
< 0.1%

sqft_basement
Real number (ℝ)

ZEROS 

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.5441
Minimum0
Maximum4820
Zeros13123
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:48.158162image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.61256
Coefficient of variation (CV)1.5181667
Kurtosis2.7144328
Mean291.5441
Median Absolute Deviation (MAD)0
Skewness1.5777307
Sum6299685
Variance195905.88
MonotonicityNot monotonic
2023-11-23T10:00:48.783485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13123
60.7%
600 221
 
1.0%
700 218
 
1.0%
500 214
 
1.0%
800 206
 
1.0%
400 184
 
0.9%
1000 149
 
0.7%
900 144
 
0.7%
300 142
 
0.7%
200 108
 
0.5%
Other values (296) 6899
31.9%
ValueCountFrequency (%)
0 13123
60.7%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 7
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

yr_built
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.9999
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:49.093438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.371463
Coefficient of variation (CV)0.014901808
Kurtosis-0.65718074
Mean1970.9999
Median Absolute Deviation (MAD)23
Skewness-0.46978236
Sum42589366
Variance862.68283
MonotonicityNot monotonic
2023-11-23T10:00:49.552689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 558
 
2.6%
2006 454
 
2.1%
2005 450
 
2.1%
2004 433
 
2.0%
2003 422
 
2.0%
2007 417
 
1.9%
1977 417
 
1.9%
1978 387
 
1.8%
1968 381
 
1.8%
2008 366
 
1.7%
Other values (106) 17323
80.2%
ValueCountFrequency (%)
1900 87
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 46
0.2%
1904 45
0.2%
1905 74
0.3%
1906 92
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 558
2.6%
2013 201
 
0.9%
2012 170
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 229
1.1%
2008 366
1.7%
2007 417
1.9%
2006 454
2.1%

yr_renovated
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.330155
Minimum0
Maximum2015
Zeros20695
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:50.105725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation401.51703
Coefficient of variation (CV)4.7612509
Kurtosis18.721546
Mean84.330155
Median Absolute Deviation (MAD)0
Skewness4.5517338
Sum1822206
Variance161215.92
MonotonicityNot monotonic
2023-11-23T10:00:50.704013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20695
95.8%
2014 91
 
0.4%
2013 37
 
0.2%
2003 36
 
0.2%
2005 35
 
0.2%
2007 35
 
0.2%
2000 35
 
0.2%
2004 26
 
0.1%
1990 25
 
0.1%
2006 24
 
0.1%
Other values (60) 569
 
2.6%
ValueCountFrequency (%)
0 20695
95.8%
1934 1
 
< 0.1%
1940 2
 
< 0.1%
1944 1
 
< 0.1%
1945 3
 
< 0.1%
1946 2
 
< 0.1%
1948 1
 
< 0.1%
1950 2
 
< 0.1%
1951 1
 
< 0.1%
1953 3
 
< 0.1%
ValueCountFrequency (%)
2015 16
 
0.1%
2014 91
0.4%
2013 37
0.2%
2012 11
 
0.1%
2011 13
 
0.1%
2010 18
 
0.1%
2009 22
 
0.1%
2008 18
 
0.1%
2007 35
 
0.2%
2006 24
 
0.1%

lat
Real number (ℝ)

Distinct5034
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.56005
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:51.240447image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.470875
median47.5718
Q347.678
95-th percentile47.749665
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.207125

Descriptive statistics

Standard deviation0.13857802
Coefficient of variation (CV)0.0029137484
Kurtosis-0.67678345
Mean47.56005
Median Absolute Deviation (MAD)0.1049
Skewness-0.4851826
Sum1027677.6
Variance0.019203867
MonotonicityNot monotonic
2023-11-23T10:00:51.774720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5491 17
 
0.1%
47.6624 17
 
0.1%
47.5322 17
 
0.1%
47.6846 17
 
0.1%
47.6886 16
 
0.1%
47.6711 16
 
0.1%
47.6955 16
 
0.1%
47.6842 15
 
0.1%
47.5402 15
 
0.1%
47.6647 15
 
0.1%
Other values (5024) 21447
99.3%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.21389
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21608
Negative (%)100.0%
Memory size337.6 KiB
2023-11-23T10:00:52.208506image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.2305
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14083016
Coefficient of variation (CV)-0.0011523253
Kurtosis1.0499275
Mean-122.21389
Median Absolute Deviation (MAD)0.1015
Skewness0.88527684
Sum-2640797.8
Variance0.019833135
MonotonicityNot monotonic
2023-11-23T10:00:52.841008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 116
 
0.5%
-122.3 111
 
0.5%
-122.362 104
 
0.5%
-122.291 100
 
0.5%
-122.363 99
 
0.5%
-122.372 99
 
0.5%
-122.288 98
 
0.5%
-122.284 95
 
0.4%
-122.357 95
 
0.4%
-122.365 94
 
0.4%
Other values (742) 20597
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

HIGH CORRELATION 

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.5832
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:53.365378image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.42147
Coefficient of variation (CV)0.34502531
Kurtosis1.5969742
Mean1986.5832
Median Absolute Deviation (MAD)410
Skewness1.1082577
Sum42926089
Variance469802.6
MonotonicityNot monotonic
2023-11-23T10:00:53.848247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 197
 
0.9%
1440 195
 
0.9%
1560 192
 
0.9%
1500 181
 
0.8%
1460 169
 
0.8%
1580 167
 
0.8%
1610 166
 
0.8%
1720 166
 
0.8%
1800 166
 
0.8%
1620 165
 
0.8%
Other values (767) 19844
91.8%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

HIGH CORRELATION 

Distinct8689
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12770.108
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:54.403311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile2004.05
Q15100
median7620
Q310083.25
95-th percentile37065.8
Maximum871200
Range870549
Interquartile range (IQR)4983.25

Descriptive statistics

Standard deviation27307.007
Coefficient of variation (CV)2.1383536
Kurtosis150.73271
Mean12770.108
Median Absolute Deviation (MAD)2505
Skewness9.505818
Sum2.759365 × 108
Variance7.4567263 × 108
MonotonicityNot monotonic
2023-11-23T10:00:54.919616image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 427
 
2.0%
4000 357
 
1.7%
6000 289
 
1.3%
7200 211
 
1.0%
4800 145
 
0.7%
7500 142
 
0.7%
8400 116
 
0.5%
4500 111
 
0.5%
3600 111
 
0.5%
5100 109
 
0.5%
Other values (8679) 19590
90.7%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.000093
Minimum0
Maximum115
Zeros38
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:55.429295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q118
median40
Q364
95-th percentile100
Maximum115
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.371463
Coefficient of variation (CV)0.66753184
Kurtosis-0.65718074
Mean44.000093
Median Absolute Deviation (MAD)23
Skewness0.46978236
Sum950754
Variance862.68283
MonotonicityNot monotonic
2023-11-23T10:00:55.951427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 558
 
2.6%
9 454
 
2.1%
10 450
 
2.1%
11 433
 
2.0%
12 422
 
2.0%
8 417
 
1.9%
38 417
 
1.9%
37 387
 
1.8%
47 381
 
1.8%
7 366
 
1.7%
Other values (106) 17323
80.2%
ValueCountFrequency (%)
0 38
 
0.2%
1 558
2.6%
2 201
 
0.9%
3 170
 
0.8%
4 130
 
0.6%
5 143
 
0.7%
6 229
1.1%
7 366
1.7%
8 417
1.9%
9 454
2.1%
ValueCountFrequency (%)
115 87
0.4%
114 29
 
0.1%
113 27
 
0.1%
112 46
0.2%
111 45
0.2%
110 74
0.3%
109 92
0.4%
108 65
0.3%
107 86
0.4%
106 94
0.4%

renovation_age
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1930.6698
Minimum0
Maximum2015
Zeros16
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size337.6 KiB
2023-11-23T10:00:56.508905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2015
Q12015
median2015
Q32015
95-th percentile2015
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation401.51703
Coefficient of variation (CV)0.20796773
Kurtosis18.721546
Mean1930.6698
Median Absolute Deviation (MAD)0
Skewness-4.5517338
Sum41717914
Variance161215.92
MonotonicityNot monotonic
2023-11-23T10:00:57.009011image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015 20695
95.8%
1 91
 
0.4%
2 37
 
0.2%
12 36
 
0.2%
10 35
 
0.2%
8 35
 
0.2%
15 35
 
0.2%
11 26
 
0.1%
25 25
 
0.1%
9 24
 
0.1%
Other values (60) 569
 
2.6%
ValueCountFrequency (%)
0 16
 
0.1%
1 91
0.4%
2 37
0.2%
3 11
 
0.1%
4 13
 
0.1%
5 18
 
0.1%
6 22
 
0.1%
7 18
 
0.1%
8 35
 
0.2%
9 24
 
0.1%
ValueCountFrequency (%)
2015 20695
95.8%
81 1
 
< 0.1%
75 2
 
< 0.1%
71 1
 
< 0.1%
70 3
 
< 0.1%
69 2
 
< 0.1%
67 1
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
62 3
 
< 0.1%

Interactions

2023-11-23T10:00:27.009947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:58:51.112717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:58:59.119338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:07.077736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:15.089376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:22.788603image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:30.161355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:37.842995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:45.092624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:49.736410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:52.727603image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:55.999358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:59.096382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:04.799067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:07.906920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:12.228041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:19.282030image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:27.555515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:58:51.688305image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:58:59.593279image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:07.591688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:15.655263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-11-23T09:59:05.437979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:12.986430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:21.008251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:28.418862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:36.175945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:43.426556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:48.990369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:52.059018image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:55.271618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:58.348729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:02.957735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:07.198147image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:10.539311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:17.580095image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:25.164106image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:33.589438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:58:57.737608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:05.861430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:13.435543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:21.429341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:28.854171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:36.640592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:43.845600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:49.144520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:52.213382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:55.457314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:58.533896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:03.392815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:07.385187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:10.917324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:18.016464image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:25.679053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:33.997742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:58:58.152417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:06.211134image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:14.147960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:21.848999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:29.257421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:37.002709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:44.225476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:49.370697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:52.371372image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:55.624323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:58.724991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:03.853719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:07.549215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:11.316681image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:18.414729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:26.106082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:34.239581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:58:58.693662image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:06.661083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:14.617676image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:22.258302image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:29.734631image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:37.397280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:44.672893image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:49.561720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:52.543440image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:55.812475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T09:59:58.903448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:04.286566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:07.716465image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:11.781074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:18.852813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-23T10:00:26.532053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-11-23T10:00:57.554386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
agebathroomsbedroomsconditionfloorsgradelatlongpricerenovation_agesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyr_builtyr_renovated
age1.000-0.567-0.1800.248-0.552-0.5010.126-0.413-0.102-0.214-0.4720.178-0.353-0.3360.0370.0160.0420.034-1.0000.214
bathrooms-0.5671.0000.5220.1300.5470.6580.0080.2620.497-0.0430.6910.1920.7460.5700.0690.0630.1140.1020.5670.043
bedrooms-0.1800.5221.0000.0240.2280.381-0.0210.1910.345-0.0170.5400.2310.6470.4440.2160.2010.0380.0000.1800.017
condition0.2480.1300.0241.000-0.288-0.167-0.022-0.0850.0180.066-0.1580.162-0.063-0.0870.1150.1180.0250.017-0.394-0.066
floors-0.5520.5470.228-0.2881.0000.5020.0250.1490.322-0.0120.599-0.2720.4010.306-0.234-0.2310.0240.0220.5520.012
grade-0.5010.6580.381-0.1670.5021.0000.1040.2230.658-0.0160.7120.0930.7170.6630.1520.1560.1430.1180.5010.016
lat0.1260.008-0.021-0.0220.0250.1041.000-0.1430.456-0.025-0.0260.1160.0310.028-0.122-0.1170.0680.034-0.1260.025
long-0.4130.2620.191-0.0850.1490.223-0.1431.0000.0630.0750.385-0.2000.2840.3800.3710.3730.0850.0970.413-0.075
price-0.1020.4970.3450.0180.3220.6580.4560.0631.000-0.1020.5420.2520.6440.5720.0750.0630.2080.3200.1020.102
renovation_age-0.214-0.043-0.0170.066-0.012-0.016-0.0250.075-0.1021.000-0.031-0.063-0.0530.006-0.008-0.0090.1080.0920.214-1.000
sqft_above-0.4720.6910.540-0.1580.5990.712-0.0260.3850.542-0.0311.000-0.1660.8430.6970.2720.2540.0890.0830.4720.031
sqft_basement0.1780.1920.2310.162-0.2720.0930.116-0.2000.252-0.063-0.1661.0000.3280.1300.0370.0300.1590.134-0.1780.063
sqft_living-0.3530.7460.647-0.0630.4010.7170.0310.2840.644-0.0530.8430.3281.0000.7470.3040.2840.1490.1400.3530.053
sqft_living15-0.3360.5700.444-0.0870.3060.6630.0280.3800.5720.0060.6970.1300.7471.0000.3600.3660.1470.0890.336-0.006
sqft_lot0.0370.0690.2160.115-0.2340.152-0.1220.3710.075-0.0080.2720.0370.3040.3601.0000.9220.0400.014-0.0370.008
sqft_lot150.0160.0630.2010.118-0.2310.156-0.1170.3730.063-0.0090.2540.0300.2840.3660.9221.0000.0350.000-0.0160.009
view0.0420.1140.0380.0250.0240.1430.0680.0850.2080.1080.0890.1590.1490.1470.0400.0351.0000.592-0.0660.096
waterfront0.0340.1020.0000.0170.0220.1180.0340.0970.3200.0920.0830.1340.1400.0890.0140.0000.5921.000-0.0290.092
yr_built-1.0000.5670.180-0.3940.5520.501-0.1260.4130.1020.2140.472-0.1780.3530.336-0.037-0.016-0.066-0.0291.000-0.214
yr_renovated0.2140.0430.017-0.0660.0120.0160.025-0.0750.102-1.0000.0310.0630.053-0.0060.0080.0090.0960.092-0.2141.000

Missing values

2023-11-23T10:00:34.736340image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-23T10:00:36.002261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedlatlongsqft_living15sqft_lot15agerenovation_age
0221900.031.00118056501.00037118001955047.5112-122.25713405650602015
1538000.032.25257072422.0003721704001951199147.7210-122.319169076396424
2180000.021.00770100001.0003677001933047.7379-122.23327208062822015
3604000.043.00196050001.0005710509101965047.5208-122.39313605000502015
4510000.032.00168080801.00038168001987047.6168-122.04518007503282015
51225000.044.5054201019301.000311389015302001047.6561-122.0054760101930142015
6257500.032.25171568192.00037171501995047.3097-122.32722386819202015
7291850.031.50106097111.00037106001963047.4095-122.31516509711522015
8229500.031.00178074701.0003710507301960047.5123-122.33717808113552015
9323000.032.50189065602.00037189002003047.3684-122.03123907570122015
pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedlatlongsqft_living15sqft_lot15agerenovation_age
21603507250.032.50227055362.00038227002003047.5389-121.88122705731122015
21604429000.032.00149011263.00038149002014047.5699-122.2881400123012015
21605610685.042.50252060232.00039252002014047.5137-122.1672520602312015
216061007500.043.50351072002.0003926009102009047.5537-122.3982050620062015
21607475000.032.50131012942.0003811801302008047.5773-122.4091330126572015
21608360000.032.50153011313.00038153002009047.6993-122.3461530150962015
21609400000.042.50231058132.00038231002014047.5107-122.3621830720012015
21610402101.020.75102013502.00037102002009047.5944-122.2991020200762015
21611400000.032.50160023882.00038160002004047.5345-122.06914101287112015
21612325000.020.75102010762.00037102002008047.5941-122.2991020135772015